Competitive learning mechanisms for clustering in general suffer from poor performance for very high dimensional ( ) data because of “curse of dimensionality” effects. In applications such as document clustering, it is customary to normalize the high dimensional input vectors to unit length, and it is sometimes also desirable to obtain balanced clusters, i.e., clusters of comparable sizes. The ...

A wide variety of distortion functions, such as squared Euclidean distance, Mahalanobis distance, Itakura-Saito distance and relative entropy, have been used for clustering. In this paper, we propose and analyze parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergences. The proposed algorithms unify centroid-based parametric clust...

Subphonetic discovery through segmental clustering is a central step in building a corpus-based synthesizer. To help decide what clustering algorithm to use we employed mergeand-split tests on English fricatives. Compared to reference of 2%, Gaussian EM achieved a misclassification rate of 6%, Kmeans 10%, while predictive CART trees performed poorly.

In this paper, we define a new notion for a clustering to be useful, called actionable clustering. This notion is motivated by applications across various domains such as in business, education, public policy and healthcare. We formalize this notion by adding a novel constraint to traditional unsupervised clustering. We argue that this notion is different from semi-supervised clustering, superv...

Data mining techniques help in business decision making and predicting behaviors and future trends. Clustering is a data mining technique used to make groups of objects that are somehow similar in characteristics. Clustering analyzes data objects without consulting a known class label or category i.e. it is an unsupervised data mining technique. Kmeans is a widely used partitional clustering al...

As we know, kmeans method is a very effective algorithm of clustering. Its most powerful feature is the scalability and simplicity. However, the most disadvantage is that we must know the number of clusters in the first place, which is usually a difficult problem in practice. In this paper, we propose a new approach– peak-searching clustering– to realize clustering without given the number of c...

Active learning is an important field of machine learning and it is becoming more widely used in case of problems where labeling the examples in the training data set is expensive. In this paper we present a clustering-based algorithm used in the Active Learning Challenge. The algorithm is based on graph clustering with normalized cuts, and uses kmeans to extract representative points from the ...

The Flocking model, first proposed by Craig Reynolds, is one of the first bio-inspired computational collective behavior models that has many popular applications, such as animation. Our early research has resulted in a flock clustering algorithm that can achieve better performance than the Kmeans or the Ant clustering algorithms for data clustering. This algorithm generates a clustering of a g...

This paper presents a text clustering system developed based on a k-means type subspace clustering algorithm to cluster large, high dimensional and sparse text data. In this algorithm, a new step is added in the k-means clustering process to automatically calculate the weights of keywords in each cluster so that the important words of a cluster can be identified by the weight values. For unders...